DEV Community: M rstmi

Is technical debt always bad, or can shipping fast ever be the right call?

M rstmi — Tue, 29 Sep 2026 16:47:53 +0000

It's not always bad, but the dramatic case of Knight Capital shows what happens when nobody owns the repayment.
On August 1, 2012, Knight Capital lost $440 million in 45 minutes, more than its entire market cap. Per the SEC's report: legacy "Power Peg" code from 2003 was deactivated but never deleted; an engineer reused its old flag for a new feature; and a deployment updated only 7 of 8 servers. The 8th server reactivated the dead code, executing roughly 4 million trades across 154 stocks. The system had even sent 97 automated warning emails beforehand that went unaddressed.
To be precise: the direct cause was a deployment failure, not simply "messy code." But the path there ran through classic technical-debt decisions, dead code never removed, shortcuts over process, no automated risk limits.
Martin Fowler's Technical Debt Quadrant offers a more nuanced view: debt can be deliberate or inadvertent, prudent or reckless. A deliberate, prudent decision ("we must ship now and pay this back later, with a plan") can be entirely rational. The dangerous quadrant is reckless debt with no ownership.
Longer breakdown here: [FullArticle]

Why are Google and Microsoft investing in nuclear power?

M rstmi — Mon, 28 Sep 2026 16:28:11 +0000

AI's electricity demand is real and growing fast, but most of the nuclear "solutions" being announced won't deliver power for years.
According to the IEA's April 2026 report, AI-focused data center electricity consumption surged 50% in 2025 alone, roughly three times faster than the growth of the broader data center industry. Total data center electricity demand is projected to nearly double, from about 485 TWh in 2025 to roughly 950 TWh by 2030. Renewable sources like solar and wind are cheaper and faster to deploy but are intermittent, while data centers need 24/7 baseload power, which is exactly what nuclear provides.
That's the real problem. But look closely at the timelines in these deals: Microsoft's restart of the Three Mile Island reactor isn't expected to be online until 2027. Google's small modular reactor (SMR) partnership with Kairos Power relies on technology that hasn't been proven at commercial scale anywhere in the US yet, with full deployment not expected until later this decade.
So the honest framing is: the problem (AI needs continuous power now) is completely real, but the announced solution (new or restarted nuclear plants) is mostly a promise for the late 2020s, not something operating today.
Longer breakdown here: [Full Article]

Why do experienced programmers still google basic things?

M rstmi — Mon, 21 Sep 2026 19:16:51 +0000

Because the skill was never memorization, it's knowing what to search for and how fast you can find it.

A 2018 survey by Blind (an anonymous workplace app for tech employees) polled over 10,400 people and found that 58% experience imposter syndrome. Broken down by company: Amazon 64.5%, Facebook 59.3%, Microsoft 56.2%, Google 56%. One Salesforce employee in the survey said they still felt this way after 14 years as an engineer.

Here's the mechanism: junior developers watch a senior colleague solve a problem quickly and assume they memorized the answer. What they don't see is that the senior developer has hit this exact problem before and knows precisely where to look, not that they never needed to look at all. The real difference between junior and senior isn't how much you've memorized; it's how quickly and accurately you can find and apply the right information.

This mirrors something interesting from AI research too: the gap between confident-sounding answers and actually correct ones. Just as language models sometimes guess confidently instead of admitting uncertainty, junior developers often see showing uncertainty (googling in front of others) as a weakness, when recognizing "I need to look this up" is actually a mature professional skill.

Longer breakdown here: [Full Article]

Why does AI confidently give wrong answers instead of saying it doesn't know?

M rstmi — Sun, 20 Sep 2026 10:58:37 +0000

OpenAI's own September 2025 research paper answers this directly, and the analogy they use is exactly what you'd expect from a room full of AI researchers: an exam.
The paper argues hallucinations aren't a mysterious glitch but a predictable result of how models are trained and evaluated. Most benchmarks grade purely on accuracy, the percentage of exactly correct answers. Under that system, saying "I don't know" scores zero, and guessing wrong also scores zero. There's no additional penalty for confidently guessing versus admitting uncertainty, so models learn that guessing is never worse and often better, just like a multiple-choice test with no penalty for wrong answers.
This isn't purely academic either. In a widely reported 2023 case, a New York lawyer used ChatGPT for legal research and submitted a court filing citing six completely fabricated court cases, complete with fake judge names and quotes. The lawyer was sanctioned once the fabrication was discovered.
The proposed fix: change how benchmarks are scored, explicitly penalizing confident wrong answers more than calibrated uncertainty, similar to how many exams already penalize guessing to discourage it.
I wrote a longer breakdown here:[My Article]

Why didn't Google release a ChatGPT competitor first, given they had the technology?

M rstmi — Wed, 16 Sep 2026 10:09:58 +0000

They had it, and deliberately held it back. That's the whole story.

Before ChatGPT launched in November 2022, Google was widely considered the global leader in AI research and had developed LaMDA, a language model reportedly more capable than the initial ChatGPT. When employees asked at an all-hands meeting whether this was a missed opportunity, Pichai and Jeff Dean said the company had comparable capabilities but that moving quickly represented a major "reputational risk" , precisely because Google was so much larger than OpenAI.

There was a second, less-discussed reason: the business model. Google makes money when you click ads next to search results. Fitting ads into conversational answers is both technically awkward and bad UX. So Google had little financial incentive to move users away from a search model generating billions annually toward an unproven one.

This is a textbook case of the Innovator's Dilemma, a company's current success becoming the main obstacle to adopting innovation that threatens it.

The human cost was real too: two of LaMDA's core engineers left Google in late 2021 after executives repeatedly blocked attempts to release the chatbot publicly. They founded what's now Character.AI.

Longer breakdown here, including how the dynamic has reversed in 2026: [My Article]

Why does exam stress paralyze some people but improve performance in others?

M rstmi — Mon, 14 Sep 2026 15:28:07 +0000

The difference comes down to how the brain appraises the situation — not the amount of stress itself.

Psychologist Jim Blascovich's Challenge vs. Threat theory explains it well: when you face a stressful situation, your brain quickly evaluates whether your resources (skill, preparation) are sufficient for the demands ahead. If yes, you enter a "challenge" state; if no, a "threat" state. These aren't just mental labels — they produce measurably different cardiovascular responses. In a challenge state, blood vessels dilate and the heart pumps more efficiently, improving blood flow to the brain. In a threat state, vessels constrict despite the faster heart rate, which impairs performance.

There's also a counterintuitive finding from Sian Beilock's research: people with higher working memory capacity are sometimes MORE likely to choke under pressure on complex tasks, because stress consumes the very cognitive resources they rely on for their advanced problem-solving strategies.

The good news: this appraisal can be shifted. Studies on "arousal reappraisal" show that simply reinterpreting physical stress symptoms as the body preparing to perform (rather than a sign of weakness) measurably improves performance.

I wrote a longer breakdown here: [Exam Stress]

Do you really need to be good at math to become a programmer?

M rstmi — Sun, 13 Sep 2026 12:58:00 +0000

I asked two working developers this exact question instead of giving the generic answer — and their perspectives, while framed differently, converged on the same point.

One said math isn't used directly in day-to-day coding, since most of what you'd need is already implemented in the language or its libraries — but having a solid math foundation helped a lot, specifically when learning databases, since that field has a direct mathematical basis. He said if he were starting from zero again, he wouldn't worry about math at all: just start with high-school-level math and build on it as your work actually demands it.

The other said math hasn't been a major help in his day-to-day work personally — whenever something was unclear, he'd just look it up and learn it in the moment. His advice to beginners: don't let math anxiety stop you from starting; plenty of people became programmers without strong math skills and are doing fine at good companies today.

Neither said math is useless — both agreed it should be learned as the need arises, not treated as a scary prerequisite.

I wrote a longer breakdown, including specific areas where math genuinely matters (algorithm complexity, graphics, hashing): [Programming-Math]

Can recruiters tell if AI wrote a resume?

M rstmi — Sat, 12 Sep 2026 14:07:12 +0000

Yes, and the numbers back it up more than you'd think.

According to Greenhouse's 2026 AI in Hiring Report (surveying 4,100+ recruiters and hiring managers), 91% have caught or suspected AI-driven deception from candidates at some point, with 63% specifically catching AI-exaggerated resume content. Separately, 62% of employers reject AI-generated resumes and cover letters that lack personalization.

The tell isn't usually perfect grammar — it's the opposite problem: genericness. AI tends to produce resumes full of interchangeable phrases ("proven track record," "results-driven professional") with vague achievements instead of specific numbers. The most reliable detection method, though, isn't the resume itself — it's the interview. When a candidate can't elaborate on a specific claim they made in writing, that mismatch gives it away regardless of who wrote the original text.

I wrote a longer breakdown of the specific patterns recruiters look for: [AI Resume]

Is Persian/Farsi really a hard language to learn?

M rstmi — Tue, 08 Sep 2026 12:14:35 +0000

Not as hard as you'd think — at least compared to what most people assume.

The US Foreign Service Institute, which has trained American diplomats since 1947, ranks languages by actual classroom hours needed to reach professional proficiency. Persian sits in Category III (~1,100 hours) — the same tier as Russian, Hindi, and Turkish. Arabic, Mandarin, Japanese, and Korean are in Category IV, requiring roughly 2,200 hours — literally double.

The reason comes down to something linguists call "linguistic distance." Persian's phonology and script are quite different from English, which pushes it out of the "easy" tier. But its grammar is comparatively simple and regular — no grammatical gender, no complex case system, no irregular root-based verb conjugation like Arabic has. That combination is exactly why it lands in the middle, not at the very top.

I wrote a more detailed breakdown of the FSI categories and what "linguistic distance" actually means here: [Languages]

Why did the Metaverse hype die down

M rstmi — Mon, 07 Sep 2026 20:15:35 +0000

Because it never solved a problem people actually had.

Meta has now lost close to $88 billion on Reality Labs since 2021 — more than the GDP of many countries. The original goal was 500,000 monthly active users on Horizon Worlds by the end of that year; the real number stayed under 200,000, and most people who tried it once never came back.

The pattern isn't unique to the metaverse. 3D TVs, Google Glass, and NFTs all followed the same arc: massive media hype and investment, followed by collapse, because none of them solved an urgent, specific problem better than existing alternatives did. There's actually a well-known framework for this — Gartner's "Hype Cycle" — which maps almost every new technology through five stages, from initial excitement to a "trough of disillusionment" and (sometimes) eventual real-world usefulness.

I wrote a longer breakdown, including whether AI might be following the same curve: [Metaverse]

Why does a rainbow have exactly seven colors?

M rstmi — Sat, 05 Sep 2026 18:48:04 +0000

Short answer: it doesn't, really — and the number seven has almost nothing to do with physics.

When Isaac Newton first split sunlight with a prism in the 1660s, he only identified five colors: red, yellow, green, blue, and violet. Ten years later, he added two more — orange and indigo — specifically to match the seven notes of a musical scale, because he believed color harmony should mirror musical harmony. There was no experimental reason for this; it was an idea he imposed on the spectrum by analogy.

Physically, a rainbow is a continuous spectrum with no real boundaries between colors. The "seven bands" we're taught to see are a cultural convention, not a property of light. Most people, in fact, can't reliably distinguish indigo from blue or violet when looking at an actual rainbow.

I wrote a more detailed breakdown here, including why the human eye's three cone types make six colors a more natural count than seven: [rainbow Article]

Is the Fibonacci sequence really everywhere in nature?

M rstmi — Wed, 02 Sep 2026 13:28:00 +0000

Not exactly — and this is one of the most common science myths out there.

Part of the story is completely real: plant leaves genuinely spiral at an angle of 137.5 degrees (the golden angle), and this comes directly from the golden ratio. The reason is mathematical: this is the one angle that never produces a repeating pattern, so no leaf ever ends up shaded directly by the one above it. Sunflower seed heads and pinecone scales follow the same rule.

But the Nautilus shell — the one everyone points to as the classic example of Fibonacci in nature — actually doesn't. Smithsonian researchers measured 80 real Nautilus shells, and the average growth ratio came out to 1.31, not 1.618 (the golden ratio). The myth traces back to an old mistake, and it was popularized further by the novel The Da Vinci Code.

I wrote a more detailed breakdown here: [FIBONACCI]